This study applied modified linear regression in machine learning (ML) to predict the direction of arrival (DoA) in cellular networks using field measurements and radiofrequency parameters. Models were developed from base station data, with preprocessing for pattern identification and formula adjustments to improve the accuracy across angle ranges. Machine learning, tested here as an additional method to traditional techniques, achieved a root mean square error (RMSE) of 3.63 to 17.93, demonstrating enhanced adaptability. While requiring substantial data and computational resources, this approach highlights machine learning’s potential as a valuable tool for DoA estimation in cellular networks.

Handover (HO) is a fundamental process in cellular networks (CNs) that ensures continuous connectivity as users move across different coverage areas. With the evolution of mobile communication technologies, the application of Machine Learning (ML) to optimize HO has emerged as a promising research avenue. However, the current literature is fragmented, spanning various cellular standards, ML approaches, datasets, and evaluation practices. This paper presents a comprehensive Systematic Literature Mapping (SLM) to consolidate and structure existing research on ML-based HO optimization. A total of 2,772 articles were identified through a carefully validated search strategy, from which 86 primary studies were selected based on strict inclusion and exclusion criteria. The analysis reveals a predominant focus on LTE (53.8%) and 5G (44.1%), a high reliance on simulated datasets (82.6%), and the frequent use of reinforcement learning (46.5%) for tasks such as handover prediction (45%), ping-pong reduction (22%), and base station selection (10%). Despite these advancements, critical gaps persist, including under-use of real-world data, limited exploration of legacy networks such as 3G, and insufficient methodological standardization. This study contributes a structured synthesis of the field, highlights research trends and deficiencies, and offers actionable insights to guide future work in ML-driven mobility management for evolving CNs.

This paper presents an overview of the findings in the handover (HO) process performance within three routes in Quito, Ecuador. We used the Net-Monitor Software to gather information from one of the three national mobile operators. Then, we used the R tool to analyze the HO performance. We analyze several performance metrics, such as HO types, HO conditions, and the ping-pong process. Analysis of the results of the outdoor drive tests demonstrate that the radio frequency (RF) parameters, such as Received Signal Strength Indicator (RSSI), Reference Signal Received Quality (RSRQ), power margin, times radio frequency measurements repeats, and HO percentage to nearest BS, are extremely important during different HO types and ping-pong processes because there are statistical differences in these measured RF parameters. The main measurement results demonstrate that RSSI difference between inter HO and intra HO is 20 dB, whereas HOs are performed when the mobile device (MS) gets farther from the base station (BS), approximately 50% of total HOs. Operator achieves a high ping-pong rate of approximately 10% of total HOs.

This paper presents a novel continuous-wave Doppler RADAR system for real-time speed measurement of moving objects, implemented using software-defined radio (SDR). Unlike traditional high-cost solutions typically found in research centers or specialized laboratories, this prototype offers a low-cost, compact, and easily deployable platform that lowers the entry barrier for experimentation and research. Operating within the 70 MHz–6 GHz range, SDR enables highly flexible signal processing; in this implementation, a 5.5 GHz carrier is selected to improve the detection precision by exploiting its reduced bandwidth for more accurate observation of frequency shifts. The carrier is modulated with a 2 kHz signal, and Doppler frequency deviations induced by object motion are processed to calculate velocity. Using a Welch spectral estimator, the system effectively reduces noise and extracts the Doppler frequency with high reliability. The prototype achieves speed measurements up to 196.36 km/h with approximately 2% error in the 0–100 km/h range, confirming its suitability for road traffic monitoring. A key innovation of this work is its single-antenna cross-polarized configuration, which simplifies hardware requirements while maintaining measurement accuracy. Furthermore, the system’s portability and open-access design make it ideal for in-vehicle applications, enabling direct deployment for automotive testing, driver-assistance research, and educational demonstrations. All design files and implementation details are openly shared, eliminating patent restrictions and encouraging adoption in low-resource academic and research environments.

The proposed work offers a concise review of the application of machine learning (ML) to cellular network handovers (HO) via the Systematic Mapping Study (SMS) methodology, emphasizing the problem areas and requirements. The key points include the paramount role of high-quality data, with meticulous data acquisition and preprocessing as vital steps in ML dataset construction. The article identifies prevalent parameters for HO enhancement and underscores the diversity of ML algorithms, aligning them with specific data input and tasks. This study establishes a robust basis for forthcoming research in applying machine learning to cellular network HOs.